code: def log_likelihood(emp_cov, precision):
      """Computes the sample mean of the log_likelihood under a covariance model
  
      computes the empirical expected log-likelihood (accounting for the
      normalization terms and scaling), allowing for universal comparison (beyond
      this software package)
  
      Parameters
      ----------
      emp_cov : 2D ndarray (n_features, n_features)
          Maximum Likelihood Estimator of covariance
  
      precision : 2D ndarray (n_features, n_features)
          The precision matrix of the covariance model to be tested
  
      Returns
      -------
      sample mean of the log-likelihood
      """
      p = precision.shape[0]
      log_likelihood_ = -np.sum(emp_cov * precision) + fast_logdet(
          precision
      )
      log_likelihood_ -= p * np.log(2 * np.pi)
      log_likelihood_ /= 2.0
      return log_likelihood_
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/covariance/empirical_covariance_.py
imports: from __future__ import division
  import warnings
  import numpy
  from scipy import linalg
  from ..base import BaseEstimator
  from ..utils import check_array
  from ..utils.extmath import fast_logdet, pinvh
label_desc: Use image node_id 1 for calling a global function with example usage: log_likelihood(emp_cov, precision) and returns: log_likelihood_